Visualization plays an important role in presenting data, ideas, analysis results etc. We need charting, diagramming and graphics tools for almost any modern applications. The availability of quality charting and visualization tools and libraries might be of great importance so we can focus on the primary.

Natural Language Processing (NLP), is a branch of Linguistics and Artificial Intelligence that deals with the interactions between computers and human language. NLP approaches process and analyze large amounts of natural language data in order to understand and make sense of the human languages that brings value and benefit.

Tutorials are everywhere,but not all of them is at the same level. The more detailed content they have, the higher is their impact for the audience. Tutorials on GitHub bring at least two benefits: collaborative and open.

Pose Estimation is a domain in computer vision where we detect and track the location and orientation of an object. Human Pose Estimation is the task to estimate the pose of a person from an image or a video by estimating the spatial locations of key body joints (keypoints - elbows, wrists, etc.). It can be used in applications such as action recognition, animation, gaming and in sport analytics as well.

Cybersecurity is nowadays of great relevance to many entities and individuals. As more and more things get connected, the threat of digital violence and abuse is very real. And it may a serious impact to the worlds normal. Thus learning cybersecurity is also relevant to many parties and individuals.

Satellite imagery are images of Earth captured by satellites, many of which currently can be access publicly. Since the resolution of satellite images varies depending on the orbit, many applications use aerial photography (taken by e.g. drone / UAV) to complement those images. In our current AI era, this satellite and aerial imagery can be used for many purposes e.g. object identification, assessment, early warning etc.

Internet of Things (IoT) is now everywhere. The rise of open hardware such as Arduino has triggered this phenomena and driven even further. The result is there are many interesting projects spanning various domains. Here we highlight some open source projects, in particular those which can be referenced for our own DIY (Do-It-Yourself) projects.

Autonomous (self-driving) vehicle is, without doubt, one of favorite applications from so many Artificial Intelligence (AI) domains. The main enabler of this autonomous vehicles is the combination of sensor technology, predictive AI and computer vision / imaging technology. From data delivered by various sensors, a 3D environment model around the vehicle is constructed. Then, the AI part processes all the information to respond accordingly, so that the vehicle moves safely. All sub-systems has to be working in concert with very short information delays.

An awesome list is a list of awesome things curated by the community. There are awesome lists about everything from CLI applications to fantasy books [Ref: Gihtub]. This post presents the big list of my current bookmark of interesting and useful awesome-lists.

One of the most explored computer vision AI/ML topics is human face detection, recognition, alignment and tracking. It can be used in diverse applications from a simple attendee or guest identification, emotion detection to a more sophisticated eKYC (Electronic Know Your Customer) and other biometric systems.

Flutter is an open-source UI toolkit created by Google. It is used to develop beautiful natively compiled applications for mobile, web and desktop from a single codebase.

Quantum physics has already changed our lives e.g. by the invention of laser and transistor. We may now be on the brink of a second quantum revolution: quantum computing and quantum communication, which could impact many sectors. These might include new breaktroughs in many areas of science, healthcare, material, energy, finance, security or entertainment.

Quality data is very important for many purposes, in particular in the area of machine learning and artificial intelligence (AI/ML). Two identical systems can deliver different results if the data fed to those systems are different, with respect to quality and/or quantity.